Abstract

BackgroundConstraint-based modeling of reconstructed genome-scale metabolic networks has been successfully applied on several microorganisms. In constraint-based modeling, in order to characterize all allowable phenotypes, network-based pathways, such as extreme pathways and elementary flux modes, are defined. However, as the scale of metabolic network rises, the number of extreme pathways and elementary flux modes increases exponentially. Uniform random sampling solves this problem to some extent to study the contents of the available phenotypes. After uniform random sampling, correlated reaction sets can be identified by the dependencies between reactions derived from sample phenotypes. In this paper, we study the relationship between extreme pathways and correlated reaction sets.ResultsCorrelated reaction sets are identified for E. coli core, red blood cell and Saccharomyces cerevisiae metabolic networks respectively. All extreme pathways are enumerated for the former two metabolic networks. As for Saccharomyces cerevisiae metabolic network, because of the large scale, we get a set of extreme pathways by sampling the whole extreme pathway space. In most cases, an extreme pathway covers a correlated reaction set in an 'all or none' manner, which means either all reactions in a correlated reaction set or none is used by some extreme pathway. In rare cases, besides the 'all or none' manner, a correlated reaction set may be fully covered by combination of a few extreme pathways with related function, which may bring redundancy and flexibility to improve the survivability of a cell. In a word, extreme pathways show strong complementary relationship on usage of reactions in the same correlated reaction set.ConclusionBoth extreme pathways and correlated reaction sets are derived from the topology information of metabolic networks. The strong relationship between correlated reaction sets and extreme pathways suggests a possible mechanism: as a controllable unit, an extreme pathway is regulated by its corresponding correlated reaction sets, and a correlated reaction set is further regulated by the organism's regulatory network.

Highlights

  • Constraint-based modeling of reconstructed genome-scale metabolic networks has been successfully applied on several microorganisms

  • We use the E. coli core model published on the web site of UCSD's systems biology research group

  • The computation of the extreme pathways for E. coli core model results in 7784 ExPas, in which 7748 are type I or II ExPas and 36 are type III ExPas (Calculation and classification of ExPas are discussed in Methods section)

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Summary

Introduction

Constraint-based modeling of reconstructed genome-scale metabolic networks has been successfully applied on several microorganisms. In constraint-based modeling, in order to characterize all allowable phenotypes, network-based pathways, such as extreme pathways and elementary flux modes, are defined. As the scale of metabolic network rises, the number of extreme pathways and elementary flux modes increases exponentially. A framework for constraintbased reconstruction and analysis (COBRA) has been developed to model and simulate the steady states of metabolic networks [2,3,4]. As reviewed in the literature [5], COBRA has been successfully applied to studying the possible phenotypes. It has attracted many attentions and gets rapid progress. With the homeostatic-steadystate hypothesis and fluxes boundaries, all allowable steady-state flux distributions are limited in a space which can be represented as

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